Several recent studies of complex networks have suggested algorithms for locating network communities, also called modules or clusters, which are mostly defined as groups of nodes with dense internal connections. Along with the rapid development of these clustering techniques, the ability of revealing overlaps between communities has become very important as well. An efficient search technique ...

A wide variety of complex networks (social, biological, information etc.) exhibit local clustering with substantial variation in the clustering coefficient (the probability of neighbors being connected). Existing models of large graphs capture power law degree distributions (BarabásiAlbert) and small-world properties (Watts-Strogatz), but only limited clustering behavior. We introduce a general...

Usually the boundary of a community in a network is drawn between nodes and thus crosses its outgoing links. If we construct overlapping communities by applying the link-clustering approach nodes and links interchange their roles. Therefore, boundaries must drawn through the nodes shared by two or more communities. For the purpose of community evaluation we define a conductance of boundary node...

Currently, there is a growing interest in identifying communities in social networks. Although there are many algorithms that suitably resolve this problem, they do not properly find overlaps among communities. This paper describes a new approach to the detection of overlapping communities based on the ideas of friendship and leadership, using a new centrality measure, called extended degree. W...

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overla...

In this paper, we present a method named OCDLS that is based on link-link similarity and SNMF (symmetric nonnegative matrix factorization) to detect overlapping communities. To begin with constructing the link-link similarity matrix, and then using SNMF to partition links, finally computing the overlapping factor, and assigning nodes to the corresponding communities according to the prespecifie...

Many complex systems can be described as networks to comprehend both the structure and the function. Community structure is one of the most important properties of complex networks. Detecting overlapping communities in networks have been more attention in recent years, but the most of approaches to this problem have been applied to the undirected networks. This paper presents a novel approach b...

Community detection in a social network is an emerging issue in the study of network system as it helps to realize the overall network structure in depth. Communities are the natural partition of network nodes into subgroups where nodes within the subgroup are densely connected but between the subgroups connections are sparser. Real world networks, including social networks have been found to p...